An Integrated Method for Multi-Objective Optimization of Multi-Pass Fe50/TiC Laser Cladding on AISI 1045 Steel based on Grey Relational Analysis and Principal Component Analysis

Coatings

Xianbin Zeng¹  Qianting Wang¹  Xu Huang²  Changrong Chen²  Guofu Lian² 

[1] School of Materials Science and Engineering, Fujian University of Technology, Fuzhou 350118, China;School of Mechanical and Automotive Engineering, Fujian University of Technology, Fuzhou 350118, China;

关键词: laser cladding;    multi-objective optimization;    grey relational analysis;    response surface methodology;    principal component analysis;   

DOI  :  10.3390/coatings10020151

来源: DOAJ

【 摘 要 】

As an essentially multi-input multi-output process, determination of optimal conditions for laser cladding normally requires multi-objective optimization. To understand multi-response coupling, the effects of processing parameters on the morphology quality of multi-pass laser claddings of Fe50/TiC on medium carbon steel AISI 1045 were investigated based on composite central design using response surface methodology. Multiple responses, including clad width, flatness, and non-fusion area, were transformed into a single objective through grey relational analysis, with weights objectively identified by principal component analysis. The correlation between grey relational grade (GRG) and process parameters was established by regression analysis. The results show that the GRG response model has excellent goodness of fit and predictive performance. A validation experiment was conducted at the process condition optimized for maximum GRG. The relative error of the predicted optimal GRG is 4.87% whereas those of interested individual objectives, i.e. clad width, flatness, and non-fusion area, are 5.73%, 2.97%, and 6.73%, respectively, which verifies the accuracy of the established model. The investigation of mechanical properties suggests the hardness of substrate can be improved from 20 HRC to 60 HRC and wear resistance to over 8.14 times better.

【 授权许可】

Unknown